Oil Core–PEG Shell Nanocarriers for In Vivo MRI Imaging
Bibliographic record
Abstract
Oil-in-water emulsions represent a promising carrier for in vivo imaging because of the possibility to convey poorly water-soluble species. To promote accumulation at the tumor site and prolong circulation time, reduction of carrier size and surface PEGylation plays a fundamental role. In this work a novel, simple method to design an oil-core/PEG-shell nanocarrier is reported. A PEG-shell is grown around a monodisperse oil-in-water nanoemulsion with a one-pot method, using the radical polymerization of poly(ethylene glycol)diacrylate. PEG polymerization is triggered by UV, obtaining a PEG-shell with tunable thickness. This core-shell nanosystem combines the eluding feature of the PEG with the ability to confine high payloads of lipophilic species. Indeed, the core is successfully loaded with a lipophilic contrast agent, namely super paramagnetic iron oxide nanocubes. Interestingly, it is demonstrated an in vitro and an in vivo MRI response of the nanocapsules. Additionally, when the nanosystem loaded with nanocubes is mixed with a fluorescent contrast agent, indo-cyanine green, a relevant in vitro photoacoustic effect is observed. Moreover, viability and cellular uptake studies show no significant cell cytotoxicity. These results, together with the choice of low cost materials and the scale up production, make this nanocarrier a potential platform for in vivo imaging.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".